A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations

Quarto
R
Academia
publication
health economics
statistics
Authors
Affiliations

Michael J Daniels

University of Florida

Gianluca Baio

University College London

Published

September 26, 2019

Abstract

Trial-based economic evaluations are typically performed on cross-sectional variables, derived from the responses for only the completers in the study …

Keywords

Missing Data, Economic Evaluations, Bayesian Statistics

Abstract

Trial-based economic evaluations are typically performed on cross-sectional variables, derived from the responses for only the completers in the study, using methods that ignore the complexities of utility and cost data (e.g. skewness and spikes). We present an alternative and more efficient Bayesian parametric approach to handle missing longitudinal outcomes in economic evaluations, while accounting for the complexities of the data. We specify a flexible parametric model for the observed data and partially identify the distribution of the missing data with partial identifying restrictions and sensitivity parameters. We explore alternative nonignorable scenarios through different priors for the sensitivity parameters, calibrated on the observed data. Our approach is motivated by, and applied to, data from a trial assessing the cost-effectiveness of a new treatment for intellectual disability and challenging behaviour.

     

Citation

BibTeX citation:
@online{gabrio2019,
  author = {Gabrio, Andrea and J Daniels, Michael and Baio, Gianluca},
  title = {A {Bayesian} {Parametric} {Approach} to {Handle} {Missing}
    {Longitudinal} {Outcome} {Data} in {Trial-Based} {Health} {Economic}
    {Evaluations}},
  volume = {183},
  number = {2},
  date = {2019-09-26},
  url = {https://academic.oup.com/jrsssa/article/183/2/607/7056293},
  doi = {10.1111/rssa.12522},
  langid = {en},
  abstract = {{[}Trial-based economic evaluations are typically
    performed on cross-sectional variables, derived from the responses
    for only the completers in the study ...{]}\{style=“font-size:
    85\%”\}}
}
For attribution, please cite this work as:
Gabrio, Andrea, Michael J Daniels, and Gianluca Baio. 2019. “A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations.” Journal of the Royal Statistical Society Series A. September 26, 2019. https://doi.org/10.1111/rssa.12522.